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Record W1585421811 · doi:10.1155/2012/494797

Wait Times for Diagnostic Colonoscopy among Outpatients with Colorectal Cancer: A Comparison with Canadian Association of Gastroenterology Targets

2012· article· en· W1585421811 on OpenAlexaffvenueabout
Michael Sey, Jamie Gregor, Paul C. Adams, Nitin Khanna, Chris Vinden, David K. Driman, Nilesh Chande

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsWestern University
Fundersnot available
KeywordsColonoscopyMedicineColorectal cancerReferralInternal medicineCancerGeneral surgeryStage (stratigraphy)Colorectal cancer screeningGastroenterologyFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Timely access to colonoscopy is a nationally recognized issue in Canada, with previous studies documenting significant wait times for a variety of indications. However, specific wait times for colonoscopy among patients diagnosed with colorectal cancer remain unknown. METHODS: A review of all outpatient cases of colorectal cancer diagnosed at colonoscopy in London, Ontario, in 2010 was performed. Wait times from the date of referral to colonoscopy were reviewed and compared with maximal wait times established by the Canadian Association of Gastroenterology (CAG) stratified according to indication. Cancer stage at the time of diagnosis was compared with colonoscopy wait times. RESULTS: A total of 106 colorectal cancer patients meeting the inclusion and exclusion criteria were included in the study. Forty-six per cent of patients waited longer than CAG targets, with a mean (± SD) wait time of 79 ± 101 days. Higher cancer stage was associated with shorter wait time, likely as a result of triaging. CONCLUSION: Long wait times for diagnostic colonoscopy among patients with colorectal cancer remain an issue, with a significant proportion of cases not meeting maximal CAG wait time targets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.239
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of GastroenterologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207